Epidemiology and Age-Related Mortality in Critically Ill Patients With Intra-Abdominal Infection or Sepsis: An International Cohort Study

Importance of Source Control in the Subgroup of Intra-Abdominal Infections for Septic Shock Patients: Analysis of 390 Cases

Background

This study aimed to evaluate the epidemiology of septic shock (SS) associated with intraabdominal infections (IAI) as well as associated mortality and efficacy of early source control in a tertiary-care educational hospital.

Methods

Patients who had SS with IAI and consulted by Infectious Diseases consultants between December 2013 and October 2022 during night shifts in our centre were analyzed retrospectively.

Results

A total number of 390 patients were included. Overall, 30-day mortality was 42.5% on day 3, while day 14 and 30 mortality rates were 63.3% and 71.3%, respectively. Source control by surgical or percutaneous operation was performed in 123 of 390 cases (31.5%), and the mortality rate was significantly lower in cases that were performed source control at any time during SS (65/123-52.8% vs 213/267-79.8%, p<0.001). In 44 of 123 cases (35.7%), source control was performed during the first 12 hours, and mortality was significantly lower in this group versus others (24/44-54.5% vs 254/346-73.4%, p=0.009). On the other hand, female gender (p<0.001, odds ratio(OR)= 2.943, 95%CI=1.714-5.054), diabetes mellitus (p= 0.014, OR=2.284, 95%CI=1.179-4.424), carbapenem-resistant Gram-negative etiology (p=0.011, OR=4.386, 95%CI=1.398-13.759), SOFA≥10 (p<0.001, OR=3.036, 95%CI=1.802-5.114), lactate >3 mg/dl (p<0.001, OR=2.764, 95%CI=1.562-4.891) and lack of source control (p=0.001, OR=2.796, 95%CI=1.523-5.133) were significantly associated with 30-day mortality in logistic regression analysis.

Conclusion

Source control has a vital importance in terms of mortality rates for IAI-related septic shock patients. Our study underscores the need for additional research, as the present analysis indicates that early source control does not manifest as a protective factor in logistic regression.

Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning

This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. A gradient boosting tree-based algorithm (XGBoost) was used for training the machine learning model to predict 30-day mortality at sepsis diagnosis time in critically ill patients. Model performance was measured in both discrimination and calibration aspects. The model was interpreted using the SHapley Additive exPlanations (SHAP) module. The 30-day mortality rate of the testing dataset was 17.9%, and 39 features were selected for the machine learning model. Model performance on the testing dataset achieved an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI 0.837-0.868) and an area under the precision-recall curves of 0.581 (95% CI 0.541-0.619). The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.

Epidemiology and risk factors for mortality in critically ill patients with pancreatic infection

Background

The AbSeS-classification defines specific phenotypes of patients with intra-abdominal infection based on the (1) setting of infection onset (community-acquired, early onset, or late-onset hospital-acquired), (2) presence or absence of either localized or diffuse peritonitis, and (3) severity of disease expression (infection, sepsis, or septic shock). This classification system demonstrated reliable risk stratification in intensive care unit (ICU) patients with intra-abdominal infection. This study aimed to describe the epidemiology of ICU patients with pancreatic infection and assess the relationship between the components of the AbSeS-classification and mortality.

Methods

This was a secondary analysis of an international observational study ("AbSeS") investigating ICU patients with intra-abdominal infection. Only patients with pancreatic infection were included in this analysis (n=165). Mortality was defined as ICU mortality within 28 days of observation for patients discharged earlier from the ICU. Relationships with mortality were assessed using logistic regression analysis and reported as odds ratio (OR) and 95% confidence interval (CI).

Results

The overall mortality was 35.2% (n=58). The independent risk factors for mortality included older age (OR=1.03, 95% CI: 1.0 to 1.1 P=0.023), localized peritonitis (OR=4.4, 95% CI: 1.4 to 13.9 P=0.011), and persistent signs of inflammation at day 7 (OR=9.5, 95% CI: 3.8 to 23.9, P<0.001) or after the implementation of additional source control interventions within the first week (OR=4.0, 95% CI: 1.3 to 12.2, P=0.013). Gram-negative bacteria were most frequently isolated (n=58, 49.2%) without clinically relevant differences in microbial etiology between survivors and non-survivors.

Conclusions

In pancreatic infection, a challenging source/damage control and ongoing pancreatic inflammation appear to be the strongest contributors to an unfavorable short-term outcome. In this limited series, essentials of the AbSeS-classification, such as the setting of infection onset, diffuse peritonitis, and severity of disease expression, were not associated with an increased mortality risk.ClinicalTrials.gov number: NCT03270345.

Ghrelin/GHSR Axis Induced M2 Macrophage and Alleviated Intestinal Barrier Dysfunction in a Sepsis Rat Model by Inactivating E2F1/NF-κB Signaling

Sepsis is an inflammatory reaction disorder state that is induced by infection. The activation and regulation of the immune system play an essential role in the development of sepsis. Our previous studies have shown that ghrelin ameliorates intestinal dysfunction in sepsis. Very little is known about the mechanism of ghrelin and its receptor (GHSR) on the intestinal barrier and the immune function of macrophage regulation. Our research is to investigate the regulatory effect and molecular mechanism of the ghrelin/GHSR axis on intestinal dysfunction and macrophage polarization in septic rats. A rat model of sepsis was established by cecal ligation and puncture (CLP) operation. Then, the sepsis rats were treated with a ghrelin receptor agonist (TZP-101) or ghrelin inhibitor (obestatin). The results suggested that TZP-101 further enhanced ghrelin and GHSR expressions in the colon and spleen of septic rats and obestatin showed the opposite results. Ghrelin/GHSR axis ameliorated colonic structural destruction and intestinal epithelial tight junction injury in septic rats. In addition, the ghrelin/GHSR axis promoted M2-type polarization of macrophages, which was characterized by the decreases of IL-1β, IL-6, and TNF-α, as well as the increase of IL-10. Mechanistically, the ghrelin/GHSR axis promoted E2F2 expression and suppressed the activation of the NF-κB signaling pathway in septic rats. Collectively, targeting ghrelin/GHSR during sepsis may represent a novel therapeutic approach for the treatment of intestinal barrier injury.

Severe infections caused by difficult-to-treat Gram-negative bacteria

PURPOSE OF REVIEW

Antimicrobial resistance (AMR) in Gram-negative bacteria (GNB) poses a significant global health concern, contributing to increased infections, mortality rates, and healthcare costs. This review discusses the main clinical manifestations, therapeutic options, and recent findings in managing antibiotic-resistant GNB, with a focus on difficult-to-treat infections.

RECENT FINDINGS

Difficult-to-treat resistance (DTR) is a novel classification that identifies GNB exhibiting intermediate or resistant phenotypes to first-line agents in the carbapenem, beta-lactam, and fluoroquinolone categories. The main pathogens implicated in severe infections include DTR Enterobacterales, DTR Pseudomonas aeruginosa , and DTR Acinetobacter baumannii. Although the clinical implications of DTR strains are still under investigation, certain studies have linked them to prolonged hospital stays and poor patient outcomes.

SUMMARY

Severe infections caused by DTR-GNB pose a formidable challenge for healthcare providers and represent a growing global health issue. The proper administration and optimization of novel antibiotics at our disposal are of paramount importance for combating bacterial resistance and improving patient prognosis.

Clinical Sepsis Phenotypes in Critically Ill Patients

Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.

Is patients' rurality associated with in-hospital sepsis death in US hospitals?

Background

The focus of this study was to explore the association of patients' rurality and other patient and hospital-related factors with in-hospital sepsis mortality to identify possible health disparities across United States hospitals.

Methods

The National Inpatient Sample was used to identify nationwide sepsis patients (n = 1,977,537, weighted n = 9,887,682) from 2016 to 2019. We used multivariate survey logistic regression models to identify predictors for how patients' rurality is associated with in-hospital death.

Findings

During the study periods, in-hospital death rates among sepsis inpatients continuously decreased (11.3% in 2016 to 9.9% in 2019) for all rurality levels. Rao-Schott Chi-Square tests demonstrated that certain patient and hospital factors had varied in-hospital death rates. Multivariate survey logistic regressions suggested that rural areas, minorities, females, older adults, low-income, and uninsured patients have higher odds of in-hospital mortality. Further, specific census divisions like New England, Middle Atlantic, and East North Central had greater in-hospital sepsis death odds.

Conclusion

Rurality was associated with increased in-hospital sepsis death across multiple patient populations and locations. Further, rurality in New England, Middle Atlantic, and East North Central locations is exceptionally high odds. In addition, minority races in rural areas also have an increased odds of in-hospital death. Therefore, rural healthcare requires a more significant influx of resources and should also include assessing patient-related factors.

Off-label use versus formal recommendations of conventional and novel antibiotics for the treatment of infections caused by multidrug-resistant bacteria

The infections caused by multidrug- and extensively drug-resistant (MDR, XDR) bacteria, including Gram-positive cocci (GPC, including methicillin-resistant Staphylococcus aureus, MDR-Streptococcus pneumoniae and vancomycin-resistant enterococci) and Gram-negative bacilli (GNB, including carbapenem-resistant [CR] Enterobacterales, CR-Pseudomonas aeruginosa and XDR/CR-Acinetobacter baumannii complex) can be quite challenging for physicians with respect to treatment decisions. Apart from complicated urinary tract and intra-abdominal infections (cUTIs, cIAIs), bloodstream infections and pneumonia, these difficult-to-treat bacteria also cause infections at miscellaneous sites (bones, joints, native/prosthetic valves and skin structures, etc.). Antibiotics like dalbavancin, oritavancin, telavancin and daptomycin are currently approved for the treatment of acute bacterial skin and skin structural infections (ABSSSIs) caused by GPC. Additionally, ceftaroline, linezolid and tigecycline have been formally approved for the treatment of community-acquired pneumonia and ABSSSI. Cefiderocol and meropenem-vaborbactam are currently approved for the treatment of cUTIs caused by XDR-GNB. The spectra of ceftazidime-avibactam and imipenem/cilastatin-relebactam are broader than that of ceftolozane-tazobactam, but these three antibiotics are currently approved for the treatment of hospital-acquired pneumonia, cIAIs and cUTIs caused by MDR-GNB. Clinical investigations of other novel antibiotics (including cefepime-zidebactam, aztreonam-avibactam and sulbactam-durlobactam) for the treatment of various infections are ongoing. Nevertheless, evidence for adequate antibiotic regimens against osteomyelitis, arthritis and infective endocarditis due to several GPC and MDR-GNB is still mostly lacking. A comprehensive review of PubMed publications was undertaken and the formal indications and off-label use of important conventional and novel antibiotics against MDR/XDR-GPC and GNB isolates cultured from miscellaneous sites are presented in this paper.

Proteomic changes associated with racial background and sepsis survival outcomes

Intra-abdominal infection is a common cause of sepsis, and intra-abdominal sepsis leads to ∼156 000 U.S. deaths annually. African American/Black adults have higher incidence and mortality rates from sepsis compared to Non-Hispanic White adults. A limited number of studies have traced survival outcomes to molecular changes; however, these studies primarily only included Non-Hispanic White adults. Our goal is to better understand molecular changes that may contribute to differences in sepsis survival in African American/Black and Non-Hispanic White adults with primary intra-abdominal infection. We employed discovery-based plasma proteomics of patient samples from the Protocolized Care for Early Septic Shock (ProCESS) cohort (N = 107). We identified 49 proteins involved in the acute phase response and complement system whose expression levels are associated with both survival outcome and racial background. Additionally, 82 proteins differentially-expressed in survivors were specific to African American/Black or Non-Hispanic White patients, suggesting molecular-level heterogeneity in sepsis patients in key inflammatory pathways. A smaller, robust set of 19 proteins were in common in African American/Black and Non-Hispanic White survivors and may represent potential universal molecular changes in sepsis. Overall, this study identifies molecular factors that may contribute to differences in survival outcomes in African American/Black patients that are not fully explained by socioeconomic or other non-biological factors.

Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients

Background

In-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes.

Materials and methods

A retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms.

Results

A total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission.

Conclusion

The prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.